From a0ad5ca84b7c648e724186631850bee15bd62399 Mon Sep 17 00:00:00 2001 From: aho Date: Wed, 3 Nov 2021 15:20:52 +0100 Subject: [PATCH 01/13] Add materials of PATC 2021 --- .../tutorial/PATC2021/hands-on_1_workflow.md | 153 +++++++++++++++ .../PATC2021/hands-on_1_workflow_ans.md | 180 ++++++++++++++++++ .../PATC2021/hands-on_2_interpolation.md | 96 ++++++++++ .../PATC2021/hands-on_2_interpolation_ans.md | 167 ++++++++++++++++ inst/doc/tutorial/PATC2021/nord3_demo.R | 102 ++++++++++ 5 files changed, 698 insertions(+) create mode 100644 inst/doc/tutorial/PATC2021/hands-on_1_workflow.md create mode 100644 inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md create mode 100644 inst/doc/tutorial/PATC2021/hands-on_2_interpolation.md create mode 100644 inst/doc/tutorial/PATC2021/hands-on_2_interpolation_ans.md create mode 100644 inst/doc/tutorial/PATC2021/nord3_demo.R diff --git a/inst/doc/tutorial/PATC2021/hands-on_1_workflow.md b/inst/doc/tutorial/PATC2021/hands-on_1_workflow.md new file mode 100644 index 0000000..53d0ceb --- /dev/null +++ b/inst/doc/tutorial/PATC2021/hands-on_1_workflow.md @@ -0,0 +1,153 @@ +# Hands-on 1: Seasonal forecast verification + +## Goal +Learn how to use the startR workflow to finish a piece of analysis, including defining and pre-processing the desired data, +defining the operation, building the workflow, and executing the operation. + +In this use case, the ensemble-adjusted Continuous Ranked Probability Score (CRPS) and the root mean square error skill score (RMSSS) are computed to verify the forecast. +To make the process faster, the required data size is small here so we can run the execution on workstation. + +## 1. Define data +The first step of the analysis is use Start() to identify the data needed. + +Hints: +(1) The experimental data files are under `/esarchive/exp/ecmwf/system4_m1/monthly_mean/`, and the observational data files are under `/esarchive/recon/ecmwf/erainterim/monthly_mean/`. +(2) The variable we want to use is 'tas'. +(3) The required time (sdate) period is November 1981 to 2016. +(4) Read global data (i.e., full spatial grids.) +(5) Take only the first time step. +(6) To make the data smaller, we only take the first two ensemble members from the experimental data. +(7) Because we are going to use the whole startR workflow, we only need to create a pointer to the data repository rather than retrieve it to the workstation. + +```r +library(startR) + +# exp data + repos_exp <- paste0('/esarchive/exp/ecmwf/system4_m1/monthly_mean/', + '$var$_f6h/$var$_$sdate$.nc') + sdates_exp <- sapply(1981:2016, function(x) paste0(x, '1101')) + + exp <- Start(dat = repos_exp, + var = ______, + sdate = sdates_exp, + time = ______, + ensemble = ______, + latitude = ______, + longitude = ______, + synonims = list(longitude = c('lon', 'longitude'), + latitude = c('lat', 'latitude')), + return_vars = list(latitude = NULL, longitude = NULL, time = NULL), + retrieve = ______) + +# obs data + repos_obs <- paste0('/esarchive/recon/ecmwf/erainterim/monthly_mean/', + '$var$_f6h/$var$_$sdate$.nc') + sdates_obs <- sapply(1981:2016, function(x) paste0(x, '11')) + + obs <- Start(dat = repos_obs, + var = ______, + sdate = sdates_obs, + time = ______, + latitude = ______, + longitude = ______, + synonims = list(longitude = c('lon', 'longitude'), + latitude = c('lat', 'latitude')), + return_vars = list(latitude = NULL, longitude = NULL, time = NULL), + retrieve = ______) +``` + +Question: + +1. What are the dimensions of these two data? + +2. What is the size of these two data? + + +## 2. Define operation and workflow +It is recommended to define the function and write Step() together, because the latter one helps you clarify the input and output dimensions of the function. + +In the self-defined function, we want to use the two functions, 'SpecsVerification::EnsCrps' and 's2dv:::.RMSSS', to calculate the skill score. +You can check the first function by typing `?SpecsVerification::EnsCrps`, and the second function on [s2dv GitLab](https://earth.bsc.es/gitlab/es/s2dv/-/blob/master/R/RMSSS.R). + +Hint: +(1) The self-defined function is for exp and obs data. Therefore, target_dims and output_dims in Step() should be a list with two vector elements. +(2) Check the functions 'EnsCrps' and '.RMSSS'. What are the minimum required dimensions of inputs of each function? These dimensions should be the 'target_dims' in Step(). +(3) To return the two skill scores ('crps' and 'rmsss') together, put them in a list with two elements. +(4) What are the dimensions of outputs? These dimensions should be the 'output_dims' in Step(). +(5) The first input of AddStep() should also be a list containing exp and obs. + +```r + # self-defined function + func <- function(exp, obs) { + # CRPS + crps <- mean(SpecsVerification::EnsCrps(exp, obs, R.new = Inf), na.rm = TRUE) + + # RMSSS + # obs only has [sdate] now. Add one more dim for it. + obs <- s2dv::InsertDim(obs, posdim = 2, lendim = 1, name = 'ensemble') + rmsss <- mean(s2dv:::.RMSSS(exp, obs, time_dim = 'sdate', dat_dim = 'ensemble', pval = FALSE)$rmsss) + + return(______) + } + step <- Step(func, target_dims = ______, + output_dims = ______) + wf <- AddStep(______, step) +``` + +Question: +1. Which dimensions are used in operation? Which dimensions are free? + +2. Can we use more dimensions as target_dims? What are the disadvantages? + + +## 3. Execute locally +To avoid potential technical problems in the connection and configuration, +we choose to run the execution locally. +Noted that it is recommended to submit jobs to HPCs if the data size is big or +the operation is heavy. + +Hint: +(1) Use the free dimensions (i.e., those are not 'target_dims' in Step()) to chunk. +(2) It is safe to divide the data into pieces of which the size each is 1/2-1/3 of RAM. In this case, the data size is only around 100Mb, so you can play with it without +worrying about crashing the workstation. +(3) You will see some error messages like "Error in R_nc4_open: No such file or directory", +which are ignorable. + +```r + res <- Compute(wf$crps, + chunks = list(______)) +``` + +## 4. Check the results + +1. Check the list structure using function `str()`. What are the items in the list? + +2. What do the dimensions of each item look like? + +3. Plot the map +Use `s2dv::PlotEquiMap` or other visualization tools to plot the map. +```r + # Get latitude and longitude from the attributes + lat <- as.vector(attr(exp, 'Variables')$common$latitude) + lon <- as.vector(attr(exp, 'Variables')$common$longitude) + + # Set the color bar + brks_crps <- seq(min(res$crps, na.rm = TRUE), max(res$crps, na.rm = TRUE), length.out = 11) + brks_rmsss <- seq(min(res$rmsss, na.rm = TRUE), max(res$rmsss, na.rm = TRUE), length.out = 11) + + library(s2dv) + # Plot crps + PlotEquiMap(______, lon, lat, + color_fun = clim.palette('yellowred'), brks = brks_crps, + filled.continents = FALSE, triangle_ends = c(TRUE, TRUE), + toptitle = 'ECMWF monthly mean tas CRPS 2012-2016', title_scale = 0.6, + fileout = '~/handson_1_fig_crps.png') + + # Plot rmsss + PlotEquiMap(______, lon, lat, + color_fun = clim.palette('yellowred'), brks = brks_rmsss, + filled.continents = FALSE, triangle_ends = c(TRUE, TRUE), + toptitle = 'ECMWF monthly mean tas RMSSS 2012-2016', title_scale = 0.6, + fileout = '~/handson_1_fig_rmsss.png') + +``` diff --git a/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md b/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md new file mode 100644 index 0000000..4e7a97b --- /dev/null +++ b/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md @@ -0,0 +1,180 @@ +# Hands-on 1: Seasonal forecast verification + +## Goal +Learn how to use the startR workflow to finish a piece of analysis, including defining and pre-processing the desired data, +defining the operation, building the workflow, and executing the operation. + +In this use case, the ensemble-adjusted Continuous Ranked Probability Score (CRPS) and the root mean square error skill score (RMSSS) are computed to verify the forecast. +To make the process faster, the required data size is small here so we can run the execution on workstation. + +## 1. Define data +The first step of the analysis is use Start() to identify the data needed. + +Hints: +(1) The experimental data files are under `/esarchive/exp/ecmwf/system4_m1/monthly_mean/`, and the observational data files are under `/esarchive/recon/ecmwf/erainterim/monthly_mean/`. +(2) The variable we want to use is 'tas'. +(3) The required time (sdate) period is November 1981 to 2016. +(4) Read global data (i.e., full spatial grids.) +(5) Take only the first time step. +(6) To make the data smaller, we only take the first two ensemble members from the experimental data. +(7) Because we are going to use the whole startR workflow, we only need to create a pointer to the data repository rather than retrieve it to the workstation. + +```r +library(startR) + +# exp data + repos_exp <- paste0('/esarchive/exp/ecmwf/system4_m1/monthly_mean/', + '$var$_f6h/$var$_$sdate$.nc') + sdates_exp <- sapply(1981:2016, function(x) paste0(x, '1101')) + + exp <- Start(dat = repos_exp, + var = 'tas', + sdate = sdates_exp, + time = indices(1), + ensemble = indices(1:2), + latitude = 'all', + longitude = 'all', + synonims = list(longitude = c('lon', 'longitude'), + latitude = c('lat', 'latitude')), + return_vars = list(latitude = NULL, longitude = NULL, time = 'sdate'), + retrieve = FALSE) + +# obs data + repos_obs <- paste0('/esarchive/recon/ecmwf/erainterim/monthly_mean/', + '$var$_f6h/$var$_$sdate$.nc') + sdates_obs <- sapply(1981:2016, function(x) paste0(x, '11')) + + obs <- Start(dat = repos_obs, + var = 'tas', + sdate = sdates_obs, + time = indices(1), + latitude = 'all', + longitude = 'all', + synonims = list(longitude = c('lon', 'longitude'), + latitude = c('lat', 'latitude')), + return_vars = list(latitude = NULL, longitude = NULL, time = 'sdate'), + retrieve = FALSE) +``` + +Question: + +1. What are the dimensions of these two data? +```r +attr(exp, 'Dimensions') + dat var sdate time ensemble latitude longitude + 1 1 36 1 2 256 512 + +attr(obs, 'Dimensions') + dat var sdate time latitude longitude + 1 1 36 1 256 512 +``` + +2. What is the size of these two data? +exp: 72Mb; obs: 36Mb + + +## 2. Define operation and workflow +It is recommended to define the function and write Step() together, because the latter one helps you clarify the input and output dimensions of the function. + +In the self-defined function, we want to use the two functions, 'SpecsVerification::EnsCrps' and 's2dv:::.RMSSS', to calculate the skill score. +You can check the first function by typing `?SpecsVerification::EnsCrps`, and the second function on [s2dv GitLab](https://earth.bsc.es/gitlab/es/s2dv/-/blob/master/R/RMSSS.R). + +Hint: +(1) The self-defined function is for exp and obs data. Therefore, target_dims and output_dims in Step() should be a list with two vector elements. +(2) Check the functions 'EnsCrps' and '.RMSSS'. What are the minimum required dimensions of inputs of each function? These dimensions should be the 'target_dims' in Step(). +(3) To return the two skill scores ('crps' and 'rmsss') together, put them in a list with two elements. +(4) What are the dimensions of outputs? These dimension should be the 'output_dims' in Step(). +(5) The first input of AddStep() should also be a list containing exp and obs. + +```r + # self-defined function + func <- function(exp, obs) { + # CRPS + crps <- mean(SpecsVerification::EnsCrps(exp, obs, R.new = Inf), na.rm = TRUE) + + # RMSSS + # obs only has [sdate] now. Add one more dim for it. + obs <- s2dv::InsertDim(obs, posdim = 2, lendim = 1, name = 'ensemble') + rmsss <- mean(s2dv:::.RMSSS(exp, obs, time_dim = 'sdate', dat_dim = 'ensemble', pval = FALSE)$rmsss) + + return(list(crps = crps, rmsss = rmsss)) + } + step <- Step(func, target_dims = list(c('sdate', 'ensemble'), c('sdate')), + output_dims = list(crps = NULL, rmsss = NULL)) + wf <- AddStep(list(exp, obs), step) +``` + +Question: +1. Which dimensions are used in operation? Which dimensions are free? +'sdate' and 'ensemble' are used in operation. The other dimensions (dat, var, time, latitude, and longitude) are not necessary for the function. These free dimensions will be subsetted by multiApply during the execution. + +2. Can we use more dimensions as target_dims? What are the disadvantages? +It still works if putting more dimensions as target_dims, but we will lose the choices for chunking in the next step. Also, the lighter the function is, the quicker the operation runs. + + + +## 3. Execute locally +To avoid potential technical problems in the connection and configuration, +we choose to run the execution locally. +Noted that it is recommended to submit jobs to HPCs if the data size is big or +the operation is heavy. + +Hint: +(1) Use the free dimensions (i.e., those are not 'target_dims' in Step()) to chunk. +(2) It is safe to divide the data into pieces of which the size each is 1/2-1/3 of RAM. In this case, the data size is only around 100Mb, so you can play with it without +worrying about crashing the workstation. +(3) You will see some error messages like "Error in R_nc4_open: No such file or directory", +which are ignorable. + +```r + res <- Compute(wf$crps, + chunks = list(latitude = 2, longitude = 2)) +``` + +## 4. Check the results + +1. Check the list structure using function `str()`. What are the items in the list? +```r +List of 2 + $ crps : num [1, 1, 1, 1:256, 1:512] 0.879 0.97 1.029 1.04 0.991 ... + $ rmsss: num [1, 1, 1, 1:256, 1:512] 0.991 0.991 0.99 0.99 0.99 ... + ... +``` + +2. What does the dimension look like? +```r +dim(res$crps) + dat var time latitude longitude + 1 1 1 256 512 +dim(res$rmsss) + dat var time latitude longitude + 1 1 1 256 512 +``` + +3. Plot the map +Use `s2dv::PlotEquiMap` or other visualization tools to plot the map. +```r + # Get latitude and longitude from the attributes + lat <- as.vector(attr(exp, 'Variables')$common$latitude) + lon <- as.vector(attr(exp, 'Variables')$common$longitude) + + # Set the color bar + brks_crps <- seq(0, max(res$crps, na.rm = TRUE), length.out = 21) + brks_rmsss <- seq(min(res$rmsss, na.rm = TRUE), max(res$rmsss, na.rm = TRUE), length.out = 21) + + library(s2dv) + # Plot crps + PlotEquiMap(res$crps[1, 1, 1, , ], lon, lat, + cols = heat.colors(length(brks_crps) - 1)[(length(brks_crps) - 1):1], + brks = brks_crps, + filled.continents = FALSE, triangle_ends = c(TRUE, TRUE), + toptitle = 'ECMWF monthly mean tas CRPS 2012-2016', title_scale = 0.6, + fileout = '~/handson_1_fig_crps.png') + + # Plot rmsss + PlotEquiMap(res$rmsss[1, 1, 1, , ], lon, lat, + color_fun = clim.palette('yellowred'), brks = brks_rmsss, + filled.continents = FALSE, triangle_ends = c(TRUE, TRUE), + toptitle = 'ECMWF monthly mean tas RMSSS 2012-2016', title_scale = 0.6, + fileout = '~/handson_1_fig_rmsss.png') +``` diff --git a/inst/doc/tutorial/PATC2021/hands-on_2_interpolation.md b/inst/doc/tutorial/PATC2021/hands-on_2_interpolation.md new file mode 100644 index 0000000..4a4bb8a --- /dev/null +++ b/inst/doc/tutorial/PATC2021/hands-on_2_interpolation.md @@ -0,0 +1,96 @@ +# Hands-on 2: Spatial grid interpolation + +## Goal +To learn how to use Start() to load the data and do the interpolation. +In this use case, we only use Start() and load the data in the local memory. +The default transformation function is startR::CDORemapper, a wrapper function of s2dv::CDORemap that uses cdo inside. + +In detail, we are going to: +(1) Learn different ways to assign longitude and latitude in Start(). +(2) Use parameter '_reorder' to specify the sorted order. +(3) Use transform-related parameters in the following practice + +## 1. Define longitude and latitude +```r + repos <- "/esarchive/exp/ecmwf/system5_m1/monthly_mean/$var$_f6h/$var$_$sdate$.nc" + var <- 'tas' + sdate <- c('20170101') + lon.min <- 0 + lon.max <- 359.9 + lat.min <- -90 + lat.max <- 90 + + data <- Start(dat = repos, + var = var, + sdate = sdate, + ensemble = indices(1), + time = 'all', + latitude = values(list(lat.min, lat.max)), + latitude_reorder = Sort(), + longitude = values(list(lon.min, lon.max)), + longitude_reorder = CircularSort(0, 360), + synonims = list(latitude = c('lat', 'latitude'), + longitude = c('lon', 'longitude')), + return_vars = list(time = 'sdate', + longitude = 'dat', + latitude = 'dat'), + retrieve = TRUE + ) +``` + +1. Run the above script. Check the dimensions, the warning messages, and the values of +longitude and latitude. What is the range of longitude and latitude? + +2. Why 'lon.max' is 359.9 but not 360? What will happen if it is 360? + +3. Now, change + - `latitude_reorder = Sort()` to `latitude_reorder = Sort(decreasing = TRUE)` + - `longitude_reorder = CircularSort(0, 360)` to `longitude_reorder = CircularSort(-180, 180)` + - Set `lon.min <- -180` and `lon.max <- 179.9` + +Check the values of longitude and latitude again. Is it different from the original script? + +4. Now, change + - `latitude = values(list(lat.min, lat.max))` to `latitude = 'all'` + - `longitude = values(list(lon.min, lon.max))` to `longitude = 'all'` + +Check the values of longitude and latitude again. Is it different from the original script? + + +## 2. Interpolation +Now, let us add in the transformation parameters. +```r + data <- Start(dat = repos, + var = var, + sdate = sdate, + ensemble = indices(1), + time = 'all', + latitude = values(list(lat.min, lat.max)), + latitude_reorder = Sort(), + longitude = values(list(lon.min, lon.max)), + longitude_reorder = CircularSort(0, 360), + ## transformation + transform = CDORemapper, + transform_extra_cells = 2, + transform_params = list(grid = 'r360x181', + method = 'conservative', + crop = c(lon.min, lon.max, + lat.min, lat.max)), + transform_vars = c('latitude', 'longitude'), + apply_indices_after_transform = FALSE, + ## + synonims = list(latitude = c('lat', 'latitude'), + longitude = c('lon', 'longitude')), + return_vars = list(time = 'sdate', + longitude = NULL, + latitude = NULL), + retrieve = TRUE + ) +``` +5. Run the above script. Check the dimensions and the values of longitude and latitude. + +6. Now, change + - `grid = 'r360x181'` to `grid = '/esarchive/exp/ecmwf/system5c3s/monthly_mean/tas_f6h/tas_20170101.nc'` + +Check the dimensions again. Is it different from the original script? + diff --git a/inst/doc/tutorial/PATC2021/hands-on_2_interpolation_ans.md b/inst/doc/tutorial/PATC2021/hands-on_2_interpolation_ans.md new file mode 100644 index 0000000..d0f5434 --- /dev/null +++ b/inst/doc/tutorial/PATC2021/hands-on_2_interpolation_ans.md @@ -0,0 +1,167 @@ +# Hands-on 2: Spatial grid interpolation + +## Goal +To learn how to use Start() to load the data and do the interpolation. +In this use case, we only use Start() and load the data in the local memory. +The default transformation function is startR::CDORemapper, a wrapper function of s2dv::CDORemap that uses cdo inside. + +In detail, we are going to: +(1) Learn different ways to assign longitude and latitude in Start(). +(2) Use parameter '_reorder' to specify the sorted order. +(3) Use transform-related parameters in the following practice + +## 1. Define longitude and latitude +```r + repos <- "/esarchive/exp/ecmwf/system5_m1/monthly_mean/$var$_f6h/$var$_$sdate$.nc" + var <- 'tas' + sdate <- c('20170101') + lon.min <- 0 + lon.max <- 359.9 + lat.min <- -90 + lat.max <- 90 + + data <- Start(dat = repos, + var = var, + sdate = sdate, + ensemble = indices(1), + time = 'all', + latitude = values(list(lat.min, lat.max)), + latitude_reorder = Sort(), + longitude = values(list(lon.min, lon.max)), + longitude_reorder = CircularSort(0, 360), + synonims = list(latitude = c('lat', 'latitude'), + longitude = c('lon', 'longitude')), + return_vars = list(time = 'sdate', + longitude = 'dat', + latitude = 'dat'), + retrieve = TRUE + ) +``` + +1. Run the above script. Check the dimensions, the warning messages, and the values of +longitude and latitude. What is the range of longitude and latitude? +```r +dim(data) + dat var sdate ensemble time latitude longitude + 1 1 1 1 7 640 1296 + +longitude <- attr(data, 'Variables')$dat1$longitude +range(longitude) +[1] 0.0000 359.7222 + +latitude <- attr(data, 'Variables')$dat1$latitude +latitude[1] +[1] -89.78488 +latitude[640] +[1] 89.78488 +``` + +2. Why 'lon.max' is 359.9 but not 360? What will happen if it is 360? +If it is 360, you only get one point of longitude. Because of `longitude_reorder = CircularSort(0, 360)`, Start() regards 0 and 360 as the same point. Therefore, we need to set a number slightly smaller than 360 but bigger than the maximum value in the original data, so we can get the whole range. + +3. Now, change + - `latitude_reorder = Sort()` to `latitude_reorder = Sort(decreasing = TRUE)` + - `longitude_reorder = CircularSort(0, 360)` to `longitude_reorder = CircularSort(-180, 180)` + - Set `lon.min <- -180` and `lon.max <- 179.9` + +Check the values of longitude and latitude again. Is it different from the original script? +```r +dim(data) + dat var sdate ensemble time latitude longitude + 1 1 1 1 7 640 1296 + +longitude <- attr(data, 'Variables')$dat1$longitude +range(longitude) +[1] -180.0000 179.7222 + +latitude <- attr(data, 'Variables')$dat1$latitude +latitude[1] +[1] 89.78488 +latitude[640] +[1] -89.78488 + +``` +The dimensions are the same. The longitude range changes to [-180, 180], and the latitude sorts from 90 to -90. + +4. Now, change + - `latitude = values(list(lat.min, lat.max))` to `latitude = 'all'` + - `longitude = values(list(lon.min, lon.max))` to `longitude = 'all'` + +Check the values of longitude and latitude again. Is it different from the original script? +```r +longitude <- attr(data, 'Variables')$dat1$longitude +range(longitude) +[1] 0.0000 359.7222 +latitude <- attr(data, 'Variables')$dat1$latitude +latitude[1] +[1] -89.78488 +latitude[640] +[1] 89.78488 +``` +The result is the same as the original script. + + +## 2. Interpolation +Now, let us add in the transformation parameters. + +```r + data <- Start(dat = repos, + var = var, + sdate = sdate, + ensemble = indices(1), + time = 'all', + latitude = values(list(lat.min, lat.max)), + latitude_reorder = Sort(), + longitude = values(list(lon.min, lon.max)), + longitude_reorder = CircularSort(0, 360), + ## transformation + transform = CDORemapper, + transform_extra_cells = 2, + transform_params = list(grid = 'r360x181', + method = 'conservative', + crop = c(lon.min, lon.max, + lat.min, lat.max)), + transform_vars = c('latitude', 'longitude'), + ## + synonims = list(latitude = c('lat', 'latitude'), + longitude = c('lon', 'longitude')), + return_vars = list(time = 'sdate', + longitude = NULL, + latitude = NULL), + retrieve = TRUE + ) +``` + +5. Run the above script. Check the dimensions and the values of longitude and latitude. +```r +dim(data) + dat var sdate ensemble time latitude longitude + 1 1 1 1 7 181 360 + +longitude <- attr(data, 'Variables')$common$longitude +range(longitude) +[1] 0 359 + +latitude <- attr(data, 'Variables')$common$latitude +range(latitude) +[1] -90 90 +``` + +6. Now, change + - `grid = 'r360x181'` to `grid = '/esarchive/exp/ecmwf/system5c3s/monthly_mean/tas_f6h/tas_20170101.nc'` + +Check the dimensions again. Is it different from the original script? +```r +dim(data) + dat var sdate ensemble time latitude longitude + 1 1 1 1 7 181 360 + +longitude <- attr(data, 'Variables')$common$longitude +range(longitude) +[1] 0 359 + +latitude <- attr(data, 'Variables')$common$latitude +range(latitude) +[1] -90 90 +``` +The result is the same as `grid = 'r360x181'`. We can use a file to specify `grid` as long as the file has a grid type well-defined. diff --git a/inst/doc/tutorial/PATC2021/nord3_demo.R b/inst/doc/tutorial/PATC2021/nord3_demo.R new file mode 100644 index 0000000..b30c41a --- /dev/null +++ b/inst/doc/tutorial/PATC2021/nord3_demo.R @@ -0,0 +1,102 @@ +library(startR) + + repos <- "/esarchive/exp/ecmwf/system5_m1/monthly_mean/$var$_f6h/$var$_$sdate$.nc" + var <- 'tas' + sdate <- c('20170101', '20170201') + lon.min <- 0 + lon.max <- 359.9 + lat.min <- -90 + lat.max <- 90 + + data <- Start(dat = repos, + var = var, + sdate = sdate, + ensemble = indices(1:50), + time = 'all', + latitude = values(list(lat.min, lat.max)), + latitude_reorder = Sort(), + longitude = values(list(lon.min, lon.max)), + longitude_reorder = CircularSort(0, 360), + synonims = list(latitude = c('lat', 'latitude'), + longitude = c('lon', 'longitude')), + return_vars = list(time = 'sdate', + longitude = NULL, latitude = NULL), + retrieve = FALSE + ) + + func <- function(x, ens_dim, conf, pval) { + # ensemble mean + ens_mean <- apply(x, which(names(dim(x)) != ens_dim), mean) + # temporal trend + trend <- s2dv:::.Trend(ens_mean, conf = conf, pval = pval)$trend + + return(trend) + } + step <- Step(func, target_dims = c('ensemble', 'time'), + output_dims = list(trend = 'stats'), + use_libraries = c('s2dv')) + wf <- AddStep(data, step, ens_dim = 1, conf = FALSE, pval = FALSE) + +#-------------------user-defined--------------------- + queue_host <- 'nord1' + temp_dir <- '/gpfs/scratch/bsc32/bsc32734/startR_hpc/' + ecflow_suite_dir <- '/home/Earth/aho/startR_local/' +#---------------------------------------------------- + + res <- Compute(wf, + chunks = list(latitude = 2, + longitude = 3), + threads_load = 2, + threads_compute = 4, + cluster = list(queue_host = queue_host, + queue_type = 'lsf', + temp_dir = temp_dir, + cores_per_job = 2, + job_wallclock = '05:00', + max_jobs = 4, + extra_queue_params = list('#BSUB -q bsc_es'), + bidirectional = FALSE, + polling_period = 10 + ), + ecflow_suite_dir = ecflow_suite_dir, + wait = FALSE + ) #$output1 + +# Save the header of res +saveRDS(res, file = '~/nord3_demo_header.Rds') + +# You can leave now. When you come back next time, just read the .Rds file +res_collect <- readRDS(save_file) +result <- Collect(res_collect, wait = TRUE) +# Save the result if needed +saveRDS(result, file = '~/nord3_demo_result.Rds') + +```r +str(result) +List of 1 + $ trend: num [1:2, 1, 1, 1:2, 1:640, 1:1296] 241.0338 -0.0714 232.0847 -0.0421 241.3164 ... + - attr(*, "startR_compute_profiling")=List of 14 + ..$ nchunks : num 6 + ..$ concurrent_chunks: num 4 + ..$ cores_per_job : num 2 + ..$ threads_load : num 2 + ..$ threads_compute : num 4 + ..$ bychunks_setup : num 4.09 + ..$ transfer : num 0 + ..$ queue : Named num [1:6] 3 5 3 3 4 3 + .. ..- attr(*, "names")= chr [1:6] "queue" "queue" "queue" "queue" ... + ..$ job_setup : Named num [1:6] 39.3 35.1 17.2 39.1 36.1 ... + .. ..- attr(*, "names")= chr [1:6] "job_setup" "job_setup" "job_setup" "job_setup" ... + ..$ load : Named num [1:6] 195 426 205 201 323 ... + .. ..- attr(*, "names")= chr [1:6] "load" "load" "load" "load" ... + ..$ compute : Named num [1:6] 481 480 472 479 491 ... + .. ..- attr(*, "names")= chr [1:6] "compute" "compute" "compute" "compute" ... + ..$ transfer_back : num 105 + ..$ merge : num 1.5 + ..$ total : num 1446 + +dim(res$trend) + stats dat var sdate latitude longitude + 2 1 1 2 640 1296 +``` + -- GitLab From ffe022bae6d786fbe028b6f122c768cc050dc980 Mon Sep 17 00:00:00 2001 From: aho Date: Wed, 3 Nov 2021 14:31:42 +0000 Subject: [PATCH 02/13] Revise the result checking --- inst/doc/tutorial/PATC2021/nord3_demo.R | 27 ++----------------------- 1 file changed, 2 insertions(+), 25 deletions(-) diff --git a/inst/doc/tutorial/PATC2021/nord3_demo.R b/inst/doc/tutorial/PATC2021/nord3_demo.R index b30c41a..d58d1c3 100644 --- a/inst/doc/tutorial/PATC2021/nord3_demo.R +++ b/inst/doc/tutorial/PATC2021/nord3_demo.R @@ -71,32 +71,9 @@ result <- Collect(res_collect, wait = TRUE) # Save the result if needed saveRDS(result, file = '~/nord3_demo_result.Rds') -```r +# Check the result str(result) -List of 1 - $ trend: num [1:2, 1, 1, 1:2, 1:640, 1:1296] 241.0338 -0.0714 232.0847 -0.0421 241.3164 ... - - attr(*, "startR_compute_profiling")=List of 14 - ..$ nchunks : num 6 - ..$ concurrent_chunks: num 4 - ..$ cores_per_job : num 2 - ..$ threads_load : num 2 - ..$ threads_compute : num 4 - ..$ bychunks_setup : num 4.09 - ..$ transfer : num 0 - ..$ queue : Named num [1:6] 3 5 3 3 4 3 - .. ..- attr(*, "names")= chr [1:6] "queue" "queue" "queue" "queue" ... - ..$ job_setup : Named num [1:6] 39.3 35.1 17.2 39.1 36.1 ... - .. ..- attr(*, "names")= chr [1:6] "job_setup" "job_setup" "job_setup" "job_setup" ... - ..$ load : Named num [1:6] 195 426 205 201 323 ... - .. ..- attr(*, "names")= chr [1:6] "load" "load" "load" "load" ... - ..$ compute : Named num [1:6] 481 480 472 479 491 ... - .. ..- attr(*, "names")= chr [1:6] "compute" "compute" "compute" "compute" ... - ..$ transfer_back : num 105 - ..$ merge : num 1.5 - ..$ total : num 1446 - dim(res$trend) - stats dat var sdate latitude longitude - 2 1 1 2 640 1296 + ``` -- GitLab From 7e0fd883a8b6cbf62548eb6cb6755ae34ad3f240 Mon Sep 17 00:00:00 2001 From: aho Date: Wed, 3 Nov 2021 14:34:27 +0000 Subject: [PATCH 03/13] Add new directory --- inst/doc/tutorial/PATC2021/Figures/.gitkeep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 inst/doc/tutorial/PATC2021/Figures/.gitkeep diff --git a/inst/doc/tutorial/PATC2021/Figures/.gitkeep b/inst/doc/tutorial/PATC2021/Figures/.gitkeep new file mode 100644 index 0000000..e69de29 -- GitLab From fdf0641191d4909dbbe48cdf226556f5c410940b Mon Sep 17 00:00:00 2001 From: aho Date: Wed, 3 Nov 2021 14:36:10 +0000 Subject: [PATCH 04/13] Upload RMSSS figures --- .../PATC2021/Figures/handson_1_fig_rmsss.png | Bin 0 -> 110949 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 inst/doc/tutorial/PATC2021/Figures/handson_1_fig_rmsss.png diff --git a/inst/doc/tutorial/PATC2021/Figures/handson_1_fig_rmsss.png b/inst/doc/tutorial/PATC2021/Figures/handson_1_fig_rmsss.png new file mode 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zt88BnXJ{paSpwi3tA2yPlP3yPxS$Aulq7OEG~7hyKMTg(bv>c{*9$Nr{NM01{zrMR b^cuatC!WkJR|(*e5r~)Ka$*Id2EP9f%30|? literal 0 HcmV?d00001 -- GitLab From 03bee93cdeb86ac56cc892e97b5f9d7630b325ef Mon Sep 17 00:00:00 2001 From: aho Date: Wed, 3 Nov 2021 14:39:31 +0000 Subject: [PATCH 06/13] Add figures --- inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md b/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md index 4e7a97b..7083f41 100644 --- a/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md +++ b/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md @@ -178,3 +178,8 @@ Use `s2dv::PlotEquiMap` or other visualization tools to plot the map. toptitle = 'ECMWF monthly mean tas RMSSS 2012-2016', title_scale = 0.6, fileout = '~/handson_1_fig_rmsss.png') ``` + +![CRPS map](./Figures/handson_1_fig_crps.png) + +![RMSSS map](./Figures/handson_1_fig_rmsss.png) + -- GitLab From ace304364d49b15b48f28e16ae0b4f83f5a9638c Mon Sep 17 00:00:00 2001 From: aho Date: Fri, 5 Nov 2021 18:18:27 +0100 Subject: [PATCH 07/13] Revise; change file path --- .../tutorial/PATC2021/hands-on_1_workflow.md | 4 +- .../PATC2021/hands-on_1_workflow_ans.md | 5 +- .../PATC2021/hands-on_2_interpolation.md | 15 +-- .../PATC2021/hands-on_2_interpolation_ans.md | 39 +----- inst/doc/tutorial/PATC2021/nord3_demo.R | 4 +- .../tutorial/PATC2021/nord3_demo_Smoothing.R | 123 ++++++++++++++++++ 6 files changed, 133 insertions(+), 57 deletions(-) create mode 100644 inst/doc/tutorial/PATC2021/nord3_demo_Smoothing.R diff --git a/inst/doc/tutorial/PATC2021/hands-on_1_workflow.md b/inst/doc/tutorial/PATC2021/hands-on_1_workflow.md index 53d0ceb..231e975 100644 --- a/inst/doc/tutorial/PATC2021/hands-on_1_workflow.md +++ b/inst/doc/tutorial/PATC2021/hands-on_1_workflow.md @@ -23,7 +23,7 @@ Hints: library(startR) # exp data - repos_exp <- paste0('/esarchive/exp/ecmwf/system4_m1/monthly_mean/', + repos_exp <- paste0('/gpfs/scratch/bsc32/bsc32734/tmp-esarchive/exp/ecmwf/system4_m1/monthly_mean/', '$var$_f6h/$var$_$sdate$.nc') sdates_exp <- sapply(1981:2016, function(x) paste0(x, '1101')) @@ -40,7 +40,7 @@ library(startR) retrieve = ______) # obs data - repos_obs <- paste0('/esarchive/recon/ecmwf/erainterim/monthly_mean/', + repos_obs <- paste0('/gpfs/scratch/bsc32/bsc32734/tmp-esarchive/recon/ecmwf/erainterim/monthly_mean/', '$var$_f6h/$var$_$sdate$.nc') sdates_obs <- sapply(1981:2016, function(x) paste0(x, '11')) diff --git a/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md b/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md index 7083f41..41997f2 100644 --- a/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md +++ b/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md @@ -23,7 +23,7 @@ Hints: library(startR) # exp data - repos_exp <- paste0('/esarchive/exp/ecmwf/system4_m1/monthly_mean/', + repos_exp <- paste0('/gpfs/scratch/bsc32/bsc32734/tmp-esarchive/exp/ecmwf/system4_m1/monthly_mean/', '$var$_f6h/$var$_$sdate$.nc') sdates_exp <- sapply(1981:2016, function(x) paste0(x, '1101')) @@ -40,7 +40,7 @@ library(startR) retrieve = FALSE) # obs data - repos_obs <- paste0('/esarchive/recon/ecmwf/erainterim/monthly_mean/', + repos_obs <- paste0('/gpfs/scratch/bsc32/bsc32734/tmp-esarchive/recon/ecmwf/erainterim/monthly_mean/', '$var$_f6h/$var$_$sdate$.nc') sdates_obs <- sapply(1981:2016, function(x) paste0(x, '11')) @@ -135,6 +135,7 @@ which are ignorable. 1. Check the list structure using function `str()`. What are the items in the list? ```r +str(res) List of 2 $ crps : num [1, 1, 1, 1:256, 1:512] 0.879 0.97 1.029 1.04 0.991 ... $ rmsss: num [1, 1, 1, 1:256, 1:512] 0.991 0.991 0.99 0.99 0.99 ... diff --git a/inst/doc/tutorial/PATC2021/hands-on_2_interpolation.md b/inst/doc/tutorial/PATC2021/hands-on_2_interpolation.md index 4a4bb8a..bcd6bd5 100644 --- a/inst/doc/tutorial/PATC2021/hands-on_2_interpolation.md +++ b/inst/doc/tutorial/PATC2021/hands-on_2_interpolation.md @@ -12,7 +12,7 @@ In detail, we are going to: ## 1. Define longitude and latitude ```r - repos <- "/esarchive/exp/ecmwf/system5_m1/monthly_mean/$var$_f6h/$var$_$sdate$.nc" + repos <- "/gpfs/scratch/bsc32/bsc32734/tmp-esarchive/exp/ecmwf/system5_m1/monthly_mean/$var$_f6h/$var$_$sdate$.nc" var <- 'tas' sdate <- c('20170101') lon.min <- 0 @@ -50,12 +50,6 @@ longitude and latitude. What is the range of longitude and latitude? Check the values of longitude and latitude again. Is it different from the original script? -4. Now, change - - `latitude = values(list(lat.min, lat.max))` to `latitude = 'all'` - - `longitude = values(list(lon.min, lon.max))` to `longitude = 'all'` - -Check the values of longitude and latitude again. Is it different from the original script? - ## 2. Interpolation Now, let us add in the transformation parameters. @@ -87,10 +81,5 @@ Now, let us add in the transformation parameters. retrieve = TRUE ) ``` -5. Run the above script. Check the dimensions and the values of longitude and latitude. - -6. Now, change - - `grid = 'r360x181'` to `grid = '/esarchive/exp/ecmwf/system5c3s/monthly_mean/tas_f6h/tas_20170101.nc'` - -Check the dimensions again. Is it different from the original script? +4. Run the above script. Check the dimensions and the values of longitude and latitude. diff --git a/inst/doc/tutorial/PATC2021/hands-on_2_interpolation_ans.md b/inst/doc/tutorial/PATC2021/hands-on_2_interpolation_ans.md index d0f5434..1b22989 100644 --- a/inst/doc/tutorial/PATC2021/hands-on_2_interpolation_ans.md +++ b/inst/doc/tutorial/PATC2021/hands-on_2_interpolation_ans.md @@ -12,7 +12,7 @@ In detail, we are going to: ## 1. Define longitude and latitude ```r - repos <- "/esarchive/exp/ecmwf/system5_m1/monthly_mean/$var$_f6h/$var$_$sdate$.nc" + repos <- "/gpfs/scratch/bsc32/bsc32734/tmp-esarchive/exp/ecmwf/system5_m1/monthly_mean/$var$_f6h/$var$_$sdate$.nc" var <- 'tas' sdate <- c('20170101') lon.min <- 0 @@ -83,23 +83,6 @@ latitude[640] ``` The dimensions are the same. The longitude range changes to [-180, 180], and the latitude sorts from 90 to -90. -4. Now, change - - `latitude = values(list(lat.min, lat.max))` to `latitude = 'all'` - - `longitude = values(list(lon.min, lon.max))` to `longitude = 'all'` - -Check the values of longitude and latitude again. Is it different from the original script? -```r -longitude <- attr(data, 'Variables')$dat1$longitude -range(longitude) -[1] 0.0000 359.7222 -latitude <- attr(data, 'Variables')$dat1$latitude -latitude[1] -[1] -89.78488 -latitude[640] -[1] 89.78488 -``` -The result is the same as the original script. - ## 2. Interpolation Now, let us add in the transformation parameters. @@ -132,7 +115,7 @@ Now, let us add in the transformation parameters. ) ``` -5. Run the above script. Check the dimensions and the values of longitude and latitude. +4. Run the above script. Check the dimensions and the values of longitude and latitude. ```r dim(data) dat var sdate ensemble time latitude longitude @@ -147,21 +130,3 @@ range(latitude) [1] -90 90 ``` -6. Now, change - - `grid = 'r360x181'` to `grid = '/esarchive/exp/ecmwf/system5c3s/monthly_mean/tas_f6h/tas_20170101.nc'` - -Check the dimensions again. Is it different from the original script? -```r -dim(data) - dat var sdate ensemble time latitude longitude - 1 1 1 1 7 181 360 - -longitude <- attr(data, 'Variables')$common$longitude -range(longitude) -[1] 0 359 - -latitude <- attr(data, 'Variables')$common$latitude -range(latitude) -[1] -90 90 -``` -The result is the same as `grid = 'r360x181'`. We can use a file to specify `grid` as long as the file has a grid type well-defined. diff --git a/inst/doc/tutorial/PATC2021/nord3_demo.R b/inst/doc/tutorial/PATC2021/nord3_demo.R index d58d1c3..af20362 100644 --- a/inst/doc/tutorial/PATC2021/nord3_demo.R +++ b/inst/doc/tutorial/PATC2021/nord3_demo.R @@ -66,7 +66,7 @@ library(startR) saveRDS(res, file = '~/nord3_demo_header.Rds') # You can leave now. When you come back next time, just read the .Rds file -res_collect <- readRDS(save_file) +res_collect <- readRDS('~/nord3_demo_header.Rds') result <- Collect(res_collect, wait = TRUE) # Save the result if needed saveRDS(result, file = '~/nord3_demo_result.Rds') @@ -75,5 +75,3 @@ saveRDS(result, file = '~/nord3_demo_result.Rds') str(result) dim(res$trend) -``` - diff --git a/inst/doc/tutorial/PATC2021/nord3_demo_Smoothing.R b/inst/doc/tutorial/PATC2021/nord3_demo_Smoothing.R new file mode 100644 index 0000000..fcb18e6 --- /dev/null +++ b/inst/doc/tutorial/PATC2021/nord3_demo_Smoothing.R @@ -0,0 +1,123 @@ +library(startR) + +# exp data + repos_exp <- paste0('/esarchive/exp/ecmwf/system5_m1/monthly_mean/', + '$var$_f6h/$var$_$sdate$.nc') + sdates_exp <- sapply(1981:2016, paste0, sprintf('%02d', 1:12), '01') + lon.min <- -80 + lon.max <- 40 + lat.min <- 20 + lat.max <- 80 + + exp <- Start(dat = repos_exp, + var = 'tas', + sdate = sdates_exp, + time = indices(1), + member = 'all', + latitude = values(list(lat.min, lat.max)), + latitude_reorder = Sort(), + longitude = values(list(lon.min, lon.max)), + longitude_reorder = CircularSort(0, 360), + transform = CDORemapper, + transform_extra_cells = 2, + transform_params = list(grid = 'r360x181', + method = 'conservative', + crop = c(lon.min, lon.max, lat.min, lat.max)), + transform_vars = c('latitude', 'longitude'), + synonims = list(longitude = c('lon', 'longitude'), + latitude = c('lat', 'latitude'), + member = c('member', 'ensemble')), + return_vars = list(latitude = NULL, longitude = NULL, time = 'sdate'), + retrieve = FALSE) + +# obs data + repos_obs <- paste0('/esarchive/recon/ecmwf/erainterim/monthly_mean/', + '$var$_f6h/$var$_$sdate$.nc') + sdates_obs <- sapply(1981:2016, paste0, sprintf('%02d', 1:12)) + + obs <- Start(dat = repos_obs, + var = 'tas', + sdate = sdates_obs, + time = indices(1), + latitude = values(list(lat.min, lat.max)), + latitude_reorder = Sort(), + longitude = values(list(lon.min, lon.max)), + longitude_reorder = CircularSort(0, 360), + transform = CDORemapper, + transform_extra_cells = 2, + transform_params = list(grid = 'r360x181', + method = 'conservative', + crop = c(lon.min, lon.max, lat.min, lat.max)), + transform_vars = c('latitude', 'longitude'), + + synonims = list(longitude = c('lon', 'longitude'), + latitude = c('lat', 'latitude')), + return_vars = list(latitude = NULL, longitude = NULL, time = 'sdate'), + retrieve = FALSE) + +func <- function(exp, obs) { + # exp: [dat, member, sdate, time] + # obs: [dat, sdate, time] + obs <- s2dv::InsertDim(obs, 2, 1, name = 'member') + clim <- s2dv::Clim(exp, obs, dat_dim = c('dat', 'member')) # [dat, member, time] + ano_exp <- s2dv::Ano(exp, clim = clim$clim_exp) # [dat, member, sdate, time] + # ensemble mean + ano_exp <- drop(apply(ano_exp, c(1, 3), mean)) # [sdate] + ano_obs <- drop(s2dv::Ano(obs, clim$clim_obs)) # [sdate] + smooth_ano_exp <- s2dv::Smoothing(ano_exp, runmeanlen = 12) + smooth_ano_obs <- s2dv::Smoothing(ano_obs, runmeanlen = 12) + return(list(exp = smooth_ano_exp, obs = smooth_ano_obs)) +} + + step <- Step(func, target_dims = list(exp = c('dat', 'member', 'sdate', 'time'), + obs = c('dat', 'sdate', 'time')), + output_dims = list(exp = 'ftime', obs = 'ftime'), + use_libraries = c('s2dv')) + wf <- AddStep(list(exp, obs), step) + +#-------------------user-defined--------------------- + queue_host <- 'nord1' + temp_dir <- '/gpfs/scratch/bsc32/bsc32734/startR_hpc/' + ecflow_suite_dir <- '/home/Earth/aho/startR_local/' +#---------------------------------------------------- + + res <- Compute(wf$exp, + chunks = list(latitude = 2, + longitude = 2), + threads_load = 2, + threads_compute = 4, + cluster = list(queue_host = queue_host, + queue_type = 'lsf', + temp_dir = temp_dir, + cores_per_job = 2, + job_wallclock = '05:00', + max_jobs = 4, + extra_queue_params = list('#BSUB -q bsc_es'), + bidirectional = FALSE, + polling_period = 10 + ), + ecflow_suite_dir = ecflow_suite_dir, + wait = FALSE + ) #$output1 + + +# Save the header of res +saveRDS(res, file = '~/nord3_demo_header.Rds') + +# You can leave now. When you come back next time, just read the .Rds file +res_collect <- readRDS('~/nord3_demo_header.Rds') +result <- Collect(res_collect, wait = TRUE) +# Save the result if needed +saveRDS(result, file = '~/nord3_demo_result.Rds') + +# Check the result +str(result) +dim(res$trend) + + + + + + + + -- GitLab From 52d94588cca0f55af86de5e6c1b0c925a078dab8 Mon Sep 17 00:00:00 2001 From: aho Date: Tue, 9 Nov 2021 15:52:13 +0100 Subject: [PATCH 08/13] Change the dir path --- inst/doc/tutorial/PATC2021/hands-on_1_workflow.md | 6 ++++-- inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md | 6 ++++-- inst/doc/tutorial/PATC2021/hands-on_2_interpolation.md | 4 +++- inst/doc/tutorial/PATC2021/hands-on_2_interpolation_ans.md | 4 +++- 4 files changed, 14 insertions(+), 6 deletions(-) diff --git a/inst/doc/tutorial/PATC2021/hands-on_1_workflow.md b/inst/doc/tutorial/PATC2021/hands-on_1_workflow.md index 231e975..b993882 100644 --- a/inst/doc/tutorial/PATC2021/hands-on_1_workflow.md +++ b/inst/doc/tutorial/PATC2021/hands-on_1_workflow.md @@ -23,7 +23,8 @@ Hints: library(startR) # exp data - repos_exp <- paste0('/gpfs/scratch/bsc32/bsc32734/tmp-esarchive/exp/ecmwf/system4_m1/monthly_mean/', + repos_exp <- paste0('/gpfs/scratch/nct00/nct00016/d3_02_R-hands_on/tmp-esarchive/', + 'exp/ecmwf/system4_m1/monthly_mean/', '$var$_f6h/$var$_$sdate$.nc') sdates_exp <- sapply(1981:2016, function(x) paste0(x, '1101')) @@ -40,7 +41,8 @@ library(startR) retrieve = ______) # obs data - repos_obs <- paste0('/gpfs/scratch/bsc32/bsc32734/tmp-esarchive/recon/ecmwf/erainterim/monthly_mean/', + repos_obs <- paste0('/gpfs/scratch/nct00/nct00016/d3_02_R-hands_on/tmp-esarchive/', + 'recon/ecmwf/erainterim/monthly_mean/', '$var$_f6h/$var$_$sdate$.nc') sdates_obs <- sapply(1981:2016, function(x) paste0(x, '11')) diff --git a/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md b/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md index 41997f2..0cfde72 100644 --- a/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md +++ b/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md @@ -23,7 +23,8 @@ Hints: library(startR) # exp data - repos_exp <- paste0('/gpfs/scratch/bsc32/bsc32734/tmp-esarchive/exp/ecmwf/system4_m1/monthly_mean/', + repos_exp <- paste0('/gpfs/scratch/nct00/nct00016/d3_02_R-hands_on/tmp-esarchive/', + 'exp/ecmwf/system4_m1/monthly_mean/', '$var$_f6h/$var$_$sdate$.nc') sdates_exp <- sapply(1981:2016, function(x) paste0(x, '1101')) @@ -40,7 +41,8 @@ library(startR) retrieve = FALSE) # obs data - repos_obs <- paste0('/gpfs/scratch/bsc32/bsc32734/tmp-esarchive/recon/ecmwf/erainterim/monthly_mean/', + repos_obs <- paste0('/gpfs/scratch/nct00/nct00016/d3_02_R-hands_on/tmp-esarchive/', + 'recon/ecmwf/erainterim/monthly_mean/', '$var$_f6h/$var$_$sdate$.nc') sdates_obs <- sapply(1981:2016, function(x) paste0(x, '11')) diff --git a/inst/doc/tutorial/PATC2021/hands-on_2_interpolation.md b/inst/doc/tutorial/PATC2021/hands-on_2_interpolation.md index bcd6bd5..1f4461b 100644 --- a/inst/doc/tutorial/PATC2021/hands-on_2_interpolation.md +++ b/inst/doc/tutorial/PATC2021/hands-on_2_interpolation.md @@ -12,7 +12,9 @@ In detail, we are going to: ## 1. Define longitude and latitude ```r - repos <- "/gpfs/scratch/bsc32/bsc32734/tmp-esarchive/exp/ecmwf/system5_m1/monthly_mean/$var$_f6h/$var$_$sdate$.nc" + repos <- paste0('/gpfs/scratch/nct00/nct00016/d3_02_R-hands_on/tmp-esarchive/', + 'exp/ecmwf/system5_m1/monthly_mean/', + '$var$_f6h/$var$_$sdate$.nc') var <- 'tas' sdate <- c('20170101') lon.min <- 0 diff --git a/inst/doc/tutorial/PATC2021/hands-on_2_interpolation_ans.md b/inst/doc/tutorial/PATC2021/hands-on_2_interpolation_ans.md index 1b22989..d2a04ff 100644 --- a/inst/doc/tutorial/PATC2021/hands-on_2_interpolation_ans.md +++ b/inst/doc/tutorial/PATC2021/hands-on_2_interpolation_ans.md @@ -12,7 +12,9 @@ In detail, we are going to: ## 1. Define longitude and latitude ```r - repos <- "/gpfs/scratch/bsc32/bsc32734/tmp-esarchive/exp/ecmwf/system5_m1/monthly_mean/$var$_f6h/$var$_$sdate$.nc" + repos <- paste0('/gpfs/scratch/nct00/nct00016/d3_02_R-hands_on/tmp-esarchive/', + 'exp/ecmwf/system5_m1/monthly_mean/', + '$var$_f6h/$var$_$sdate$.nc') var <- 'tas' sdate <- c('20170101') lon.min <- 0 -- GitLab From bf72a058e8f295d3d87dd04098a5fe1acbe17f70 Mon Sep 17 00:00:00 2001 From: aho Date: Tue, 9 Nov 2021 16:30:33 +0100 Subject: [PATCH 09/13] Fix the dir path in instruction and format fix --- inst/doc/tutorial/PATC2021/hands-on_1_workflow.md | 2 +- .../tutorial/PATC2021/hands-on_1_workflow_ans.md | 15 +++++++-------- .../tutorial/PATC2021/hands-on_2_interpolation.md | 8 ++++---- .../PATC2021/hands-on_2_interpolation_ans.md | 6 +++--- 4 files changed, 15 insertions(+), 16 deletions(-) diff --git a/inst/doc/tutorial/PATC2021/hands-on_1_workflow.md b/inst/doc/tutorial/PATC2021/hands-on_1_workflow.md index b993882..233c6f8 100644 --- a/inst/doc/tutorial/PATC2021/hands-on_1_workflow.md +++ b/inst/doc/tutorial/PATC2021/hands-on_1_workflow.md @@ -11,7 +11,7 @@ To make the process faster, the required data size is small here so we can run t The first step of the analysis is use Start() to identify the data needed. Hints: -(1) The experimental data files are under `/esarchive/exp/ecmwf/system4_m1/monthly_mean/`, and the observational data files are under `/esarchive/recon/ecmwf/erainterim/monthly_mean/`. +(1) The data paths use two '$' as the wildcard, and it can be given any names you like. (2) The variable we want to use is 'tas'. (3) The required time (sdate) period is November 1981 to 2016. (4) Read global data (i.e., full spatial grids.) diff --git a/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md b/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md index 0cfde72..aafbc21 100644 --- a/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md +++ b/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md @@ -10,14 +10,13 @@ To make the process faster, the required data size is small here so we can run t ## 1. Define data The first step of the analysis is use Start() to identify the data needed. -Hints: -(1) The experimental data files are under `/esarchive/exp/ecmwf/system4_m1/monthly_mean/`, and the observational data files are under `/esarchive/recon/ecmwf/erainterim/monthly_mean/`. -(2) The variable we want to use is 'tas'. -(3) The required time (sdate) period is November 1981 to 2016. -(4) Read global data (i.e., full spatial grids.) -(5) Take only the first time step. -(6) To make the data smaller, we only take the first two ensemble members from the experimental data. -(7) Because we are going to use the whole startR workflow, we only need to create a pointer to the data repository rather than retrieve it to the workstation. +Hints:(1) The data paths use two '$' as the wildcard, and it can be given any names you like. +(2) The variable we want to use is 'tas'. +(3) The required time (sdate) period is November 1981 to 2016. +(4) Read global data (i.e., full spatial grids.) +(5) Take only the first time step. +(6) To make the data smaller, we only take the first two ensemble members from the experimental data. +(7) Because we are going to use the whole startR workflow, we only need to create a pointer to the data repository rather than retrieve it to the workstation. ```r library(startR) diff --git a/inst/doc/tutorial/PATC2021/hands-on_2_interpolation.md b/inst/doc/tutorial/PATC2021/hands-on_2_interpolation.md index 1f4461b..9630f74 100644 --- a/inst/doc/tutorial/PATC2021/hands-on_2_interpolation.md +++ b/inst/doc/tutorial/PATC2021/hands-on_2_interpolation.md @@ -5,10 +5,10 @@ To learn how to use Start() to load the data and do the interpolation. In this use case, we only use Start() and load the data in the local memory. The default transformation function is startR::CDORemapper, a wrapper function of s2dv::CDORemap that uses cdo inside. -In detail, we are going to: -(1) Learn different ways to assign longitude and latitude in Start(). -(2) Use parameter '_reorder' to specify the sorted order. -(3) Use transform-related parameters in the following practice +In detail, we are going to: +(1) Learn different ways to assign longitude and latitude in Start(). +(2) Use parameter '_reorder' to specify the sorted order. +(3) Use transform-related parameters. ## 1. Define longitude and latitude ```r diff --git a/inst/doc/tutorial/PATC2021/hands-on_2_interpolation_ans.md b/inst/doc/tutorial/PATC2021/hands-on_2_interpolation_ans.md index d2a04ff..b8fa3d0 100644 --- a/inst/doc/tutorial/PATC2021/hands-on_2_interpolation_ans.md +++ b/inst/doc/tutorial/PATC2021/hands-on_2_interpolation_ans.md @@ -6,9 +6,9 @@ In this use case, we only use Start() and load the data in the local memory. The default transformation function is startR::CDORemapper, a wrapper function of s2dv::CDORemap that uses cdo inside. In detail, we are going to: -(1) Learn different ways to assign longitude and latitude in Start(). -(2) Use parameter '_reorder' to specify the sorted order. -(3) Use transform-related parameters in the following practice +(1) Learn different ways to assign longitude and latitude in Start(). +(2) Use parameter '_reorder' to specify the sorted order. +(3) Use transform-related parameters. ## 1. Define longitude and latitude ```r -- GitLab From 18801038e9c506f2fd922596e2334176a8444b2d Mon Sep 17 00:00:00 2001 From: aho Date: Tue, 9 Nov 2021 16:00:45 +0000 Subject: [PATCH 10/13] format fix --- inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md b/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md index aafbc21..c4d7393 100644 --- a/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md +++ b/inst/doc/tutorial/PATC2021/hands-on_1_workflow_ans.md @@ -10,7 +10,8 @@ To make the process faster, the required data size is small here so we can run t ## 1. Define data The first step of the analysis is use Start() to identify the data needed. -Hints:(1) The data paths use two '$' as the wildcard, and it can be given any names you like. +Hints: +(1) The data paths use two '$' as the wildcard, and it can be given any names you like. (2) The variable we want to use is 'tas'. (3) The required time (sdate) period is November 1981 to 2016. (4) Read global data (i.e., full spatial grids.) -- GitLab From 87e4461ef268d185d08715e014df4c7a119a6bf3 Mon Sep 17 00:00:00 2001 From: aho Date: Wed, 17 Nov 2021 17:13:27 +0100 Subject: [PATCH 11/13] Remove unused file --- .../tutorial/PATC2021/nord3_demo_Smoothing.R | 123 ------------------ 1 file changed, 123 deletions(-) delete mode 100644 inst/doc/tutorial/PATC2021/nord3_demo_Smoothing.R diff --git a/inst/doc/tutorial/PATC2021/nord3_demo_Smoothing.R b/inst/doc/tutorial/PATC2021/nord3_demo_Smoothing.R deleted file mode 100644 index fcb18e6..0000000 --- a/inst/doc/tutorial/PATC2021/nord3_demo_Smoothing.R +++ /dev/null @@ -1,123 +0,0 @@ -library(startR) - -# exp data - repos_exp <- paste0('/esarchive/exp/ecmwf/system5_m1/monthly_mean/', - '$var$_f6h/$var$_$sdate$.nc') - sdates_exp <- sapply(1981:2016, paste0, sprintf('%02d', 1:12), '01') - lon.min <- -80 - lon.max <- 40 - lat.min <- 20 - lat.max <- 80 - - exp <- Start(dat = repos_exp, - var = 'tas', - sdate = sdates_exp, - time = indices(1), - member = 'all', - latitude = values(list(lat.min, lat.max)), - latitude_reorder = Sort(), - longitude = values(list(lon.min, lon.max)), - longitude_reorder = CircularSort(0, 360), - transform = CDORemapper, - transform_extra_cells = 2, - transform_params = list(grid = 'r360x181', - method = 'conservative', - crop = c(lon.min, lon.max, lat.min, lat.max)), - transform_vars = c('latitude', 'longitude'), - synonims = list(longitude = c('lon', 'longitude'), - latitude = c('lat', 'latitude'), - member = c('member', 'ensemble')), - return_vars = list(latitude = NULL, longitude = NULL, time = 'sdate'), - retrieve = FALSE) - -# obs data - repos_obs <- paste0('/esarchive/recon/ecmwf/erainterim/monthly_mean/', - '$var$_f6h/$var$_$sdate$.nc') - sdates_obs <- sapply(1981:2016, paste0, sprintf('%02d', 1:12)) - - obs <- Start(dat = repos_obs, - var = 'tas', - sdate = sdates_obs, - time = indices(1), - latitude = values(list(lat.min, lat.max)), - latitude_reorder = Sort(), - longitude = values(list(lon.min, lon.max)), - longitude_reorder = CircularSort(0, 360), - transform = CDORemapper, - transform_extra_cells = 2, - transform_params = list(grid = 'r360x181', - method = 'conservative', - crop = c(lon.min, lon.max, lat.min, lat.max)), - transform_vars = c('latitude', 'longitude'), - - synonims = list(longitude = c('lon', 'longitude'), - latitude = c('lat', 'latitude')), - return_vars = list(latitude = NULL, longitude = NULL, time = 'sdate'), - retrieve = FALSE) - -func <- function(exp, obs) { - # exp: [dat, member, sdate, time] - # obs: [dat, sdate, time] - obs <- s2dv::InsertDim(obs, 2, 1, name = 'member') - clim <- s2dv::Clim(exp, obs, dat_dim = c('dat', 'member')) # [dat, member, time] - ano_exp <- s2dv::Ano(exp, clim = clim$clim_exp) # [dat, member, sdate, time] - # ensemble mean - ano_exp <- drop(apply(ano_exp, c(1, 3), mean)) # [sdate] - ano_obs <- drop(s2dv::Ano(obs, clim$clim_obs)) # [sdate] - smooth_ano_exp <- s2dv::Smoothing(ano_exp, runmeanlen = 12) - smooth_ano_obs <- s2dv::Smoothing(ano_obs, runmeanlen = 12) - return(list(exp = smooth_ano_exp, obs = smooth_ano_obs)) -} - - step <- Step(func, target_dims = list(exp = c('dat', 'member', 'sdate', 'time'), - obs = c('dat', 'sdate', 'time')), - output_dims = list(exp = 'ftime', obs = 'ftime'), - use_libraries = c('s2dv')) - wf <- AddStep(list(exp, obs), step) - -#-------------------user-defined--------------------- - queue_host <- 'nord1' - temp_dir <- '/gpfs/scratch/bsc32/bsc32734/startR_hpc/' - ecflow_suite_dir <- '/home/Earth/aho/startR_local/' -#---------------------------------------------------- - - res <- Compute(wf$exp, - chunks = list(latitude = 2, - longitude = 2), - threads_load = 2, - threads_compute = 4, - cluster = list(queue_host = queue_host, - queue_type = 'lsf', - temp_dir = temp_dir, - cores_per_job = 2, - job_wallclock = '05:00', - max_jobs = 4, - extra_queue_params = list('#BSUB -q bsc_es'), - bidirectional = FALSE, - polling_period = 10 - ), - ecflow_suite_dir = ecflow_suite_dir, - wait = FALSE - ) #$output1 - - -# Save the header of res -saveRDS(res, file = '~/nord3_demo_header.Rds') - -# You can leave now. When you come back next time, just read the .Rds file -res_collect <- readRDS('~/nord3_demo_header.Rds') -result <- Collect(res_collect, wait = TRUE) -# Save the result if needed -saveRDS(result, file = '~/nord3_demo_result.Rds') - -# Check the result -str(result) -dim(res$trend) - - - - - - - - -- GitLab From 8027b2681ead0a328830ad48fbd23b513eb0424d Mon Sep 17 00:00:00 2001 From: aho Date: Wed, 17 Nov 2021 17:22:02 +0100 Subject: [PATCH 12/13] Restructure the tutorial folder and add the slides --- .../tutorial/PATC2021/PATC2021_handson.pdf | Bin 0 -> 3152424 bytes .../{ => internal_training_20200902}/crps.png | Bin .../hands-on_part1.md | 0 .../hands-on_part1_ans.md | 0 .../hands-on_part2.md | 0 .../hands-on_part2_ans.md | 0 .../nord3_demo.R | 0 .../rmsss.png | Bin .../startR_tutorial_20200902.pdf | Bin 9 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 inst/doc/tutorial/PATC2021/PATC2021_handson.pdf rename inst/doc/tutorial/{ => internal_training_20200902}/crps.png 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